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38:34ENA founder used Codex to build the Norwegian tutor he wantedGuest William Imoh · Host Claire VoWilliam Imoh describes a voice app built around conversation, then shows how role-based agents support his company.
19:41ENA high-reliability automation starts by routing the hard cases awayMatthew Jewkes · Standard CyberneticsMatthew Jewkes describes separating critical errors, ambiguous decisions and unusual exceptions in a biopharma indexing workflow.
49:56ENBarbara Oakley: use AI to support learning, not replace itBarbara Oakley · Oakland UniversityShe connects memory, practice and curiosity to the challenge of judging AI-generated answers.
22:01ENZach Lloyd: make the software factory measurable and editableZach Lloyd · WarpWarp’s CEO proposes recording agent runs, scoring failures and testing changes to the workflow on real tasks.
27:35ENUber traded an always-green trunk for faster AI-scale mergesPreetam Dwivedi, Manjari Akella · UberIts merge queue now relaxes some conflicts and validates the combined state after landing, with a verified-good fallback.
15:25ENHassan and Omar El Mghari: When an AI financial forecast is right for the wrong reasonsHassan El Mghari, Omar El Mghari · Together AIA Costco membership-fee model passed basic checks and landed close to reported results, but an audit uncovered offsetting assumptions.
14:38ENJemima Abu: Give coding agents accessibility rules from the startJemima Abu · CAISA product engineer shows how semantic HTML, reusable components and explicit agent skills can help teams build more usable interfaces.
21:12ENTom Moor: Give coding agents the product context that code cannot showTom Moor · LinearLinear’s workflow carries customer requests, past decisions, review history and human feedback from intake through implementation and review.
15:35ENMislav Ivanda: Choose an execution boundary before an agent frameworkMislav Ivanda · DaytonaThe Daytona engineer compares local approvals, embedded runtimes and sandboxed machines for running agent-generated code.
15:21ENAshwin Paranjape: Testing financial agents against earnings consensusAshwin Paranjape · Samaya AISamaya compares seven models on more than 450 earnings releases using time-limited data, a bias-corrected baseline and several scoring methods.
17:57ENMaty Bohacek: Can AI agents impersonate people to pass verification?Maty Bohacek · Stanford UniversityA controlled vault experiment tests whether agents use available audio and video impersonation tools, and how safety alignment changes the result.
18:56ENPratyusha Duvvuri: Making AI token spending understandable to engineering and financePratyusha Duvvuri · CiscoA Cisco engineering lead explains why provider bills, team ownership and return on investment require different views of the same AI usage.
45:21ENTheo: AI coding makes maintaining the development environment the jobTheo Browne · Ping LabsFrom Twitch to T3 Code, Theo argues that engineers must remove the friction and feedback failures that block agents.
19:24ENRichard Feldman: keep the agent conversation with the codeRichard Feldman · DeltaA shared thread can preserve decisions, reviews and handoff context that otherwise gets lost between teammates.
19:50ENKeiji Kanazawa rebuilt his agent system after reading its codeKeiji Kanazawa · MicrosoftA three-week refactor produced fake agents and expensive tests. He rebuilt around explicit goals and evaluations.
20:51ENJason Mancuso: Scaling agent reinforcement learning across mixed GPUsJason Mancuso · ModalModal combines asynchronous rollouts, mismatch corrections and compressed weight updates to train coding agents across a fragmented GPU fleet.
18:32ENWhy a plausible contract analysis missed $800,000Natalia Connolly, Kevin Madura · AlixPartnersAlixPartners’ Natalia Connolly and Kevin Madura show how missing links, renewal clauses and silent access errors can derail an AI procurement analysis.
23:15ENJay Parikh: GitHub must adapt as coding agents change developmentGuest Jay Parikh · Host Shawn WangGitHub's scale, model choices and security are changing as agents join developers, says Microsoft CoreAI's Jay Parikh.
58:33ENGemini’s product chief on testing models and building ahead of themGuest Tulsee Doshi · Host Lenny RachitskyTulsee Doshi explains a small-eval starting point, her 60-20-20 product framework and why model behavior itself is a user experience.
46:40ENAshley Alexander: How AI Helps Me Care for My Parents With CancerGuest Ashley Alexander · Host Peter YangA caregiver uses AI to prepare for appointments, organize records and ask better questions—while keeping doctors at the center of treatment.
12:38ENWhat makes AI-generated work feel like slop?Host Chris WilliamsonChris Williamson and his guest ask creators to explain their judgments, and to distinguish a tool’s defaults from intentional craft.
18:56ENHow Citadel Securities builds a memory that catches troubled projectsJoao Wenzel · Citadel SecuritiesJoao Wenzel describes an agent system that weighs evidence, tracks aging facts and tests its warnings against what happened next.
18:39ENHow Ramp tests a bookkeeping agent before and after launchRyan Stevens · RampRyan Stevens explains synthetic businesses, all-or-nothing scoring, skill ablation and a two-period test for agent memory.
16:09ENHow Range’s financial planners built their own AI workflowsMatt Ragone · RangeMatt Ragone’s team used real cases and existing advice standards to build skills, while keeping a certified planner’s review before client delivery.
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